An intuitive metric to quantify and communicate tropical cyclone rainfall hazard
Abstract
Rainfall from tropical cyclones (TCs)-the most frequent cause of TC-related deaths in the United States-poses significant hazard to human life and property. Climate models indicate, with high confidence, that TC rainfall rates in the North Atlantic will increase due to global climate change. Furthermore, the mean translational speed of North Atlantic TCs over land has decreased by 20% since the mid-20thcentury. These changes have implications for future rainfall hazard as more intense, slower-moving storms are more likely to generate extreme rainfall totals.
Characterizing and communicating the magnitude and likelihood of extreme rainfall from TCs remains challenging. The traditional Saffir-Simpson scale does not consider a TC's rainfall potential. Other metrics, such as recurrence intervals—traditionally used by engineers and hydrologists—have become more widely-used in the popular media; however, they are often misunderstood by the public and have important statistical limitations. We introduce an alternative metric—the "extreme rain multiplier" (ERM)—which expresses TC rainfall as a multiple of the two-year rainfall value for the same geographic location. This allows individuals to connect (or "anchor," in cognitive psychology terms) the magnitude of a TC rainfall event to the magnitude of more common rain events at that location. ERM correctly identifies damaging TC rainfall events that would have been classified as "weak" using wind-based metrics and can be used, in conjunction with rainfall forecast data, to characterize TC rainfall hazard days before landfall. We present a retrospective analysis of ERM values for TCs in the U.S. from 1948 to 2018. Hurricane Harvey (2017) had the highest ERM value during this period, underlining the storm's extreme nature. However, ERM is "regionally invariant," allowing us to identify locally-extreme TC rainfall events throughout the United States (often associated with absolute rainfall magnitudes significantly less than Harvey). We also connect ERM to recurrence interval concepts to provide improve analysis of how frequently extreme TC rainfall events occur. Further application of ERM to global TC data identifies differences in extreme TC rainfall between ocean basins, providing additional uses of ERM as a hazard quantification and communication tool.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMNH54A..08B
- Keywords:
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- 4308 Other;
- NATURAL HAZARDS;
- 4328 Risk;
- NATURAL HAZARDS;
- 4351 International organizations and natural disasters;
- NATURAL HAZARDS;
- 4352 Interaction between science and disaster management authorities;
- NATURAL HAZARDS